User authentication through typing biometrics features

Araújo, Lívia C. F.; Sucupira, Luiz H. R.; Lizárraga, Miguel Gustavo; Ling, Lee Luan; Yabu-uti, João Baptista Tadanobu

doi:10.1109/tsp.2004.839903

Cited by 170 publications

(39 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Table 2 shows EERs for some of the algorithms with the best performance on the recent CMU dataset. The first algorithm given in Table 2 is an anomaly detector that uses Manhattan Distance [1] [32]. This method arrives at the mean of timing samples and the absolute mean standard deviation for each feature [32].…”

Section: Results Of Implementationmentioning

confidence: 99%

See 1 more Smart Citation

Keystroke Dynamics Based Authentication Using Information Sets

Bhatia¹,

Hanmandlu²

2017

JMP

View full text Add to dashboard Cite

This paper presents keystroke dynamics based authentication system using the information set concept. Two types of membership functions (MFs) are computed: one based on the timing features of all the samples and another based on the timing features of a single sample. These MFs lead to two types of information components (spatial and temporal) which are concatenated and modified to produce different feature types. Two Component Information Set (TCIS) is proposed for keystroke dynamics based user authentication. The keystroke features are converted into TCIS features which are then classified by SVM, Random Forest and proposed Convex Entropy Based Hanman Classifier. The TCIS features are capable of representing the spatial and temporal uncertainties. The performance of the proposed features is tested on CMU benchmark dataset in terms of error rates (FAR, FRR, EER) and accuracy of the features. In addition, the proposed features are also tested on Android Touch screen based Mobile Keystroke Dataset. The TCIS features improve the performance and give lower error rates and better accuracy than that of the existing features in literature.

show abstract

Section: Results Of Implementationmentioning

confidence: 99%

“…The first algorithm given in Table 2 is an anomaly detector that uses Manhattan Distance [1] [32]. This method arrives at the mean of timing samples and the absolute mean standard deviation for each feature [32]. Given a test feature vector, a distance score is calculated using the following scaled Manhattan Distance:…”

Section: Results Of Implementationmentioning

confidence: 99%

Keystroke Dynamics Based Authentication Using Information Sets

Bhatia¹,

Hanmandlu²

2017

JMP

View full text Add to dashboard Cite

show abstract

“…Results using this as a feature vary from a FAR of 0.5% and an FRR rate of 3.1% when only considering the space time, to equal error rates of between 4% and 12% depending on the algorithm [15]. When also considering the interval between the same point on successive keystrokes, a 1.45% FRR and a 1.89% FAR has been obtained [16].…”

Section: Related Workmentioning

confidence: 99%

On the Design and Analysis of a Biometric Authentication System Using Keystroke Dynamics

Cockell

Halak

2020

Cryptography

View full text Add to dashboard Cite

this paper proposes a portable hardware token for user's authentication; it is based on the use of keystroke dynamics to verify users biometrically. The proposed approach allows for a multifactor authentication scheme, in which a user cannot be granted access unless they provide a correct password on a hardware token and their biometric signature. The latter is extracted while the user is typing their password. This paper explains the design rationale of the proposed system and provides a comprehensive insight in the development of a hardware prototype of the same. The paper also presents a feasibility study that included a systematic analysis based on training data obtained from 32 users. Our results show that dynamic keystroke can be employed to construct a cost-efficient solution for biometric user authentication with an average error rate of 4.5%

show abstract

“…Em geral, após a coleta dos dados brutos, é executada uma fase de extração de características consideradas relevantes para o processo de reconhecimento do usuário (Costa et al, 2005;Cavalcanti, 2005;Araújo, Sucupira, Lizarraga, Ling, & Yabu-Uti, 2005). Tal fase consiste em gerar novos atributos a partir dos dados coletados.…”

Section: Reconhecimento De Usuário Baseado Em Dinâmica Da Digitaçãounclassified

Dinâmica da Digitação Aplicada à Autenticação Periódica de Usuários em Ambientes Virtuais de Aprendizagem

Cruz

Duarte

Goldschmidt

2017

RBIE

View full text Add to dashboard Cite

Resumo A autenticação de usuário nos ambientes virtuais de aprendizagem (AVAs) existentes é, em geral, pontual e intrusiva, ocorrendo no momento em que o usuário Abstract The authentication of users on a Virtual Learning Environment (VLE) is, in general, punctual and intrusive, occurring when the user connects to the environment, by typing his password. Such approach allows, after the initial login, that unauthenticated users take the role of authenticated users and perform tasks in the environment, causing, among other things, distortions in the perception about the academic performance of students. The objective of this work is, thus, to propose a mechanism to execute periodic and non-intrusive authentications of users in VLEs.The proposed mechanism uses machine learning techniques to build recognition models based on the keystroke dynamics of users and it is also independent of the used VLE. A prototype of the proposed mechanism, integrated with Moodle, was implemented and applied to a postgraduate course with seventeen users. The recognition models generated by the prototype in the case study showed a performance above 92% of accuracy, which is a positive indication about the viability of the utilization of the proposed mechanism.

show abstract

User authentication through typing biometrics features

Cited by 170 publications

References 10 publications

Keystroke Dynamics Based Authentication Using Information Sets

Keystroke Dynamics Based Authentication Using Information Sets

On the Design and Analysis of a Biometric Authentication System Using Keystroke Dynamics

Dinâmica da Digitação Aplicada à Autenticação Periódica de Usuários em Ambientes Virtuais de Aprendizagem

Contact Info

Product

Resources

About